Thermodynamic and kinetic selection in evolving chemical mixtures
Bibliographic record
Abstract
Complex or even relatively simple mixtures undergoing chemical transformations tend to combinatorically explode, i.e., a large number of different chemical species arise due to the large number of ways to combine them. The rise of chemical selectivity was one of the most important steps towards life and its emergence presents one of the most challenging questions in the origins of life research. Nevertheless, recent empirical work has shown that under some conditions, combinatorial compression, i.e., a reduced number of species compared to that expected by combinatorics, is observed. The mechanisms underlying the observed compression in the chemical space are yet to be elucidated. In this paper we combined thermodynamic and kinetic theory together with computer simulations to track the evolution of species (i.e., changes in concentrations) under a wide range of parameter scenarios. We have studied and defined a set of rules that are required for compression: (i) chemical connectivity, (ii) thermodynamic or kinetic dominance, (iii) continuous feeding of the ‘compressor’, and (iv) appropriate temperature or reaction time. Our results shed new light on the way in which chemical evolution operates at the very fundamental level and can guide future experiments of chemical evolution towards generation of chemical spaces that can potentially self-maintain high reactivity and open-ended evolution.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".